GPU comparison
GeForce RTX 5060 vs GeForce RTX 5060 Ti 16GB
Compared on the specs that decide a purchase: memory, bandwidth, power, and which workloads each card actually fits — gaming, creative, or local models when that is the job.
Specifications verified October 2, 2026. Launch MSRP is not today's price — in September 2026 the memory shortage pushed street prices 1.2×–3× above it; the notes row below carries a dated lowest UK/US price where we have one.

NVIDIA
GeForce RTX 5060
8GB VRAM · 448 GB/s

NVIDIA
GeForce RTX 5060 Ti 16GB
16GB VRAM · 448 GB/s
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| Specification | GeForce RTX 5060 | GeForce RTX 5060 Ti 16GB |
|---|---|---|
| Vendor | ||
| Vendor | NVIDIA | NVIDIA |
| Memory | ||
| Memory | 8GB | Winner: 16GB |
| Usable for a model | ||
| Usable for a model | 8GB | Winner: 16GB |
| Bandwidth | ||
| Bandwidth | 448 GB/s | 448 GB/s |
| Segment | ||
| Segment | consumer | consumer |
| Released | ||
| Released | May 19, 2025 | April 16, 2025 |
| Launch MSRP | ||
| Launch MSRP | ~$299~£221 | ~$429~£318 |
| Notes | ||
| Notes | 8GB GDDR7; ASUS specifies 28Gbps on a 128-bit bus, yielding 448GB/s theoretical bandwidth. NVIDIA's launch notice lists May 19, 2025 availability starting at $299. Smaller quantized models may fit; use the site's estimate and check actual runtime memory. Local-model fit is a planning estimate; model files, context, cache, backend and driver support determine actual memory use and speed. | 16GB GDDR7 variant. ASUS specifies 28Gbps memory on a 128-bit bus, yielding 448GB/s theoretical bandwidth (28 × 128 ÷ 8). NVIDIA announced April 16, 2025 availability starting at $429. Local-model fit is a planning estimate; model files, context, cache, backend and driver support determine actual memory use and speed. |
| UK street price | ||
| UK street price | Search Amazon (opens in new tab) | Search Amazon (opens in new tab) |
Local models
Estimated local-model fit
Planning estimates from published VRAM and a weights-plus-overhead heuristic. Context length, KV cache, quantization and runtime can change actual memory needs.
| Model | GeForce RTX 5060 (est.) | GeForce RTX 5060 Ti 16GB (est.) |
|---|---|---|
| Qwen 3 4B4B parameters | ||
| Qwen 3 4B4B parameters | Est. fit | Est. fit |
| Qwen 3.8 27B27B parameters | ||
| Qwen 3.8 27B27B parameters | Est. no fit | Est. no fit |
| Gemma 4 31B30.7B parameters | ||
| Gemma 4 31B30.7B parameters | Est. no fit | Est. no fit |
| Llama 3.1 8B Instruct8B parameters | ||
| Llama 3.1 8B Instruct8B parameters | Est. fit | Est. fit |
| Qwen 3 14B14.8B parameters | ||
| Qwen 3 14B14.8B parameters | Est. no fit | Est. fit |
| Gemma 3 27B27B parameters | ||
| Gemma 3 27B27B parameters | Est. no fit | Est. no fit |
| Qwen 3 30B-A3B30.5B parameters | ||
| Qwen 3 30B-A3B30.5B parameters | Est. no fit | Est. no fit |
| Qwen 3 32B32.8B parameters | ||
| Qwen 3 32B32.8B parameters | Est. no fit | Est. no fit |
| Llama 3.3 70B70B parameters | ||
| Llama 3.3 70B70B parameters | Est. no fit | Est. no fit |
| Muse Glimmer 30B29.6B parameters | ||
| Muse Glimmer 30B29.6B parameters | Est. no fit | Est. no fit |
| Mixtral 8x22B141B parameters | ||
| Mixtral 8x22B141B parameters | Est. no fit | Est. no fit |
| Qwen 3 235B-A22B235B parameters | ||
| Qwen 3 235B-A22B235B parameters | Est. no fit | Est. no fit |
| Qwen 3 Coder 480B-A35B480B parameters | ||
| Qwen 3 Coder 480B-A35B480B parameters | Est. no fit | Est. no fit |
| DeepSeek V4-Flash284B parameters | ||
| DeepSeek V4-Flash284B parameters | Est. no fit | Est. no fit |
Common questions
GeForce RTX 5060 vs GeForce RTX 5060 Ti 16GB
Answered from the verified figures on this page rather than general guidance.
Which costs less at launch MSRP, GeForce RTX 5060 or GeForce RTX 5060 Ti 16GB?
Which is better for high-refresh gaming, GeForce RTX 5060 or GeForce RTX 5060 Ti 16GB?
Which has more VRAM for gaming and creative work, GeForce RTX 5060 or GeForce RTX 5060 Ti 16GB?
For local inference only — which holds larger models, GeForce RTX 5060 or GeForce RTX 5060 Ti 16GB?
For local inference only — is GeForce RTX 5060 or GeForce RTX 5060 Ti 16GB faster at generating tokens?
For local inference only — can either run a 70B model?
If one card holds a model the other cannot, that capacity difference usually outweighs a bandwidth advantage. A model that spills to system memory can be substantially slower, but the actual impact depends on the backend, transfer path, context, and workload.